Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R

About

Features

• Guides students through setting up their computer for data science, understanding how the pieces fit together, and successfully using them to solve real problems
• Introduces R, RStudio, git, GitHub, Markdown, Shiny, and other leading tools
• Covers everything from preparing raw data to creating beautiful, sharable visualizations
• Anticipates questions and demystifies complex ideas, reflecting the authors’ experience introducing data science to thousands of students

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Description

• Copyright 2019
• Dimensions: 7" x 9-1/8"
• Pages: 384
• Edition: 1st

• Book
• ISBN-10: 0-13-513310-6
• ISBN-13: 978-0-13-513310-1

The Foundational Hands-On Skills You Need to Dive into Data Science

“Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.”

–From the foreword by Jared Lander, series editor

Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience.

Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales.

Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to

• Install your complete data science environment, including R and RStudio
• Manage projects efficiently, from version tracking to documentation
• Host, manage, and collaborate on data science projects with GitHub
• Master R language fundamentals: syntax, programming concepts, and data structures
• Load, format, explore, and restructure data for successful analysis
• Interact with databases and web APIs
• Master key principles for visualizing data accurately and intuitively
• Produce engaging, interactive visualizations with ggplot and other R packages
• Transform analyses into sharable documents and sites with R Markdown
• Create interactive web data science applications with Shiny
• Collaborate smoothly as part of a data science team

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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Extras

Author's Site

Visit this site for code listings from the authors: https://programming-for-data-science.github.io/

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Sample Content

Online Sample Chapter

Reshaping Data with tidyr

Sample Pages

Download the sample pages (includes Chapter 14 and index)

Table of Contents

Foreword xi

Preface xiii

Acknowledgments xvii

About the Authors xix

Part I: Getting Started 1

Chapter 1: Setting Up Your Computer 3

1.1 Setting up Command Line Tools 4

1.2 Installing git 5

1.3 Creating a GitHub Account 6

1.4 Selecting a Text Editor 6

1.5 Downloading the R Language 7

1.6 Downloading RStudio 8

Chapter 2: Using the Command Line 9

2.1 Accessing the Command Line 9

2.2 Navigating the File System 11

2.3 Managing Files 15

2.4 Dealing with Errors 18

2.5 Directing Output 20

2.6 Networking Commands 20

Part II: Managing Projects 25

Chapter 3: Version Control with git and GitHub 27

3.1 What Is git? 27

3.2 Configuration and Project Setup 30

3.3 Tracking Project Changes 32

3.4 Storing Projects on GitHub 36

3.5 Accessing Project History 40

3.6 Ignoring Files from a Project 42

Chapter 4: Using Markdown for Documentation 45

4.1 Writing Markdown 45

4.2 Rendering Markdown 48

Part III: Foundational R Skills 51

Chapter 5: Introduction to R 53

5.1 Programming with R 53

5.2 Running R Code 54

5.3 Including Comments 58

5.4 Defining Variables 58

5.5 Getting Help 63

Chapter 6: Functions 69

6.1 What Is a Function? 69

6.2 Built-in R Functions 71

6.3 Loading Functions 73

6.4 Writing Functions 75

6.5 Using Conditional Statements 79

Chapter 7: Vectors 81

7.1 What Is a Vector? 81

7.2 Vectorized Operations 83

7.3 Vector Indices 88

7.4 Vector Filtering 90

7.5 Modifying Vectors 92

Chapter 8: Lists 95

8.1 What Is a List? 95

8.2 Creating Lists 96

8.3 Accessing List Elements 97

8.4 Modifying Lists 100

8.5 Applying Functions to Lists with lapply() 102

Part IV: Data Wrangling 105

Chapter 9: Understanding Data 107

9.1 The Data Generation Process 107

9.2 Finding Data 108

9.3 Types of Data 110

9.4 Interpreting Data 112

9.5 Using Data to Answer Questions 116

Chapter 10: Data Frames 119

10.1 What Is a Data Frame? 119

10.2 Working with Data Frames 120

10.3 Working with CSV Data 124

Chapter 11: Manipulating Data with dplyr 131

11.1 A Grammar of Data Manipulation 131

11.2 Core dplyr Functions 132

11.3 Performing Sequential Operations 139

11.4 Analyzing Data Frames by Group 142

11.5 Joining Data Frames Together 144

11.6 dplyr in Action: Analyzing Flight Data 148

Chapter 12: Reshaping Data with tidyr 155

12.1 What Is “Tidy” Data? 155

12.2 From Columns to Rows: gather() 157

12.3 From Rows to Columns: spread() 158

12.4 tidyr in Action: Exploring Educational Statistics 160

Chapter 13: Accessing Databases 167

13.1 An Overview of Relational Databases 167

13.2 A Taste of SQL 171

13.3 Accessing a Database from R 175

Chapter 14: Accessing Web APIs 181

14.1 What Is a Web API? 181

14.2 RESTful Requests 182

14.3 Accessing Web APIs from R 189

14.4 Processing JSON Data 191

14.5 APIs in Action: Finding Cuban Food in Seattle 197

Part V: Data Visualization 205

Chapter 15: Designing Data Visualizations 207

15.1 The Purpose of Visualization 207

15.2 Selecting Visual Layouts 209

15.3 Choosing Effective Graphical Encodings 220

15.4 Expressive Data Displays 227

15.5 Enhancing Aesthetics 229

Chapter 16: Creating Visualizations with ggplot2 231

16.1 A Grammar of Graphics 231

16.2 Basic Plotting with ggplot2 232

16.3 Complex Layouts and Customization 238

16.4 Building Maps 248

16.5 ggplot2 in Action: Mapping Evictions in San Francisco 252

Chapter 17: Interactive Visualization in R 257

17.1 The plotly Package 258

17.2 The rbokeh Package 261

17.3 The leaflet Package 263

17.4 Interactive Visualization in Action: Exploring Changes to the City of Seattle 266

Part VI: Building and Sharing Applications 273

Chapter 18: Dynamic Reports with R Markdown 275

18.1 Setting Up a Report 275

18.2 Integrating Markdown and R Code 279

18.3 Rendering Data and Visualizations in Reports 281

18.4 Sharing Reports as Websites 284

18.5 R Markdown in Action: Reporting on Life Expectancy 287

Chapter 19: Building Interactive Web Applications with Shiny 293

19.1 The Shiny Framework 293

19.2 Designing User Interfaces 299

19.3 Developing Application Servers 306

19.4 Publishing Shiny Apps 309

19.5 Shiny in Action: Visualizing Fatal Police Shootings 311

Chapter 20: Working Collaboratively 319

20.1 Tracking Different Versions of Code with Branches 319

20.2 Developing Projects Using Feature Branches 329

20.3 Collaboration Using the Centralized Work[1]ow 331

20.4 Collaboration Using the Forking Work[1]ow 335

Chapter 21: Moving Forward 341

21.1 Statistical Learning 341

21.2 Other Programming Languages 342

21.3 Ethical Responsibilities 343

Index 345

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